{
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python388jvsc74a57bd0238245627cfcfafd0885ed67ad6caa77217e3abcf58754ab647d5e61878a8b28",
   "display_name": "Python 3.8.8 64-bit ('tensorflow': conda)"
  }
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 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "tensorflow version:2.3.0\n"
     ]
    }
   ],
   "source": [
    "# 引入TensorFlow\n",
    "import tensorflow as tf\n",
    "print(\"tensorflow version:{}\".format(tf.__version__));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "pandas version:1.2.4\n"
     ]
    }
   ],
   "source": [
    "# 引入pandas\n",
    "import pandas as pd \n",
    "print(\"pandas version:{}\".format(pd.__version__));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "       second     third\n",
       "0    0.067732  3.176513\n",
       "1    0.427810  3.816464\n",
       "2    0.995731  4.550095\n",
       "3    0.738336  4.256571\n",
       "4    0.981083  4.560815\n",
       "..        ...       ...\n",
       "195  0.257017  3.585821\n",
       "196  0.833735  4.374394\n",
       "197  0.070095  3.213817\n",
       "198  0.527070  3.952681\n",
       "199  0.116163  3.129283\n",
       "\n",
       "[200 rows x 2 columns]"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>second</th>\n      <th>third</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.067732</td>\n      <td>3.176513</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.427810</td>\n      <td>3.816464</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.995731</td>\n      <td>4.550095</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.738336</td>\n      <td>4.256571</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.981083</td>\n      <td>4.560815</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>195</th>\n      <td>0.257017</td>\n      <td>3.585821</td>\n    </tr>\n    <tr>\n      <th>196</th>\n      <td>0.833735</td>\n      <td>4.374394</td>\n    </tr>\n    <tr>\n      <th>197</th>\n      <td>0.070095</td>\n      <td>3.213817</td>\n    </tr>\n    <tr>\n      <th>198</th>\n      <td>0.527070</td>\n      <td>3.952681</td>\n    </tr>\n    <tr>\n      <th>199</th>\n      <td>0.116163</td>\n      <td>3.129283</td>\n    </tr>\n  </tbody>\n</table>\n<p>200 rows × 2 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "# 加载数据\n",
    "data = pd.read_csv(\"../data/testdata/regression.txt\",delimiter=\"\\t\",names=[\"first\",\"second\",\"third\"],usecols=[\"second\",\"third\"]);\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x26766a45be0>"
      ]
     },
     "metadata": {},
     "execution_count": 15
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 绘制数据集\n",
    "import matplotlib.pyplot as plt \n",
    "plt.scatter(data.second,data.third)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential_4\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ndense (Dense)                (None, 1)                 2         \n=================================================================\nTotal params: 2\nTrainable params: 2\nNon-trainable params: 0\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 构建模型\n",
    "x = data.second\n",
    "y = data.third\n",
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Dense(1,input_shape=(1,)))\n",
    "model.summary()\n",
    "# 编译模型\n",
    "model.compile(optimizer='adam',loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4758/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4759/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4760/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4761/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4762/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4763/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4764/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4765/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4766/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4767/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4768/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4769/5000\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 0.0068\n",
      "Epoch 4770/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4771/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4772/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4773/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4774/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4775/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4776/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4777/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4778/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4779/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4780/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4781/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4782/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4783/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4784/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4785/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4786/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4787/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4788/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4789/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4790/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4791/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4792/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4793/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4794/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4795/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4796/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4797/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4798/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4799/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4800/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4801/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4802/5000\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 0.0068\n",
      "Epoch 4803/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4804/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4805/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4806/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4807/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4808/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4809/5000\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 0.0068\n",
      "Epoch 4810/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4811/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4812/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4813/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4814/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4815/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4816/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4817/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4818/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4819/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4820/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4821/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4822/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4823/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4824/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4825/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4826/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4827/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4828/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4829/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4830/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4831/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4832/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4833/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4834/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4835/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4836/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4837/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4838/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4839/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4840/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4841/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4842/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4843/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4844/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4845/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4846/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4847/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4848/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4849/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4850/5000\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 0.0068\n",
      "Epoch 4851/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4852/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4853/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4854/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4855/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4856/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4857/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4858/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4859/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4860/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4861/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4862/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4863/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4864/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4865/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4866/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4867/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4868/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4869/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4870/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4871/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4872/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4873/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4874/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4875/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4876/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4877/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4878/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4879/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4880/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4881/5000\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 0.0068\n",
      "Epoch 4882/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4883/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4884/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4885/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4886/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4887/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4888/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4889/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4890/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4891/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4892/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4893/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4894/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4895/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4896/5000\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 0.0068\n",
      "Epoch 4897/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4898/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4899/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4900/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4901/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4902/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4903/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4904/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4905/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4906/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4907/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4908/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4909/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4910/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4911/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4912/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4913/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4914/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4915/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4916/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4917/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4918/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4919/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4920/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4921/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4922/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4923/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4924/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4925/5000\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 0.0068\n",
      "Epoch 4926/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4927/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4928/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4929/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4930/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4931/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4932/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4933/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4934/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4935/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4936/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4937/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4938/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4939/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4940/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4941/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4942/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4943/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4944/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4945/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4946/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4947/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4948/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4949/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4950/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4951/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4952/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4953/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4954/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4955/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4956/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4957/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4958/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4959/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4960/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4961/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4962/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4963/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4964/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4965/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4966/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4967/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4968/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4969/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4970/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4971/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4972/5000\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 0.0068\n",
      "Epoch 4973/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4974/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4975/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4976/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4977/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4978/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4979/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4980/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4981/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4982/5000\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 0.0068\n",
      "Epoch 4983/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4984/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4985/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4986/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4987/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4988/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4989/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4990/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4991/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4992/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4993/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4994/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4995/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4996/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4997/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4998/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 4999/5000\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 0.0068\n",
      "Epoch 5000/5000\n",
      "7/7 [==============================] - 0s 997us/step - loss: 0.0068\n"
     ]
    }
   ],
   "source": [
    "# 训练模型\n",
    "history = model.fit(x,y,epochs=1500);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "WARNING:tensorflow:Keras is training/fitting/evaluating on array-like data. Keras may not be optimized for this format, so if your input data format is supported by TensorFlow I/O (https://github.com/tensorflow/io) we recommend using that to load a Dataset instead.\n",
      "WARNING:tensorflow:Keras is training/fitting/evaluating on array-like data. Keras may not be optimized for this format, so if your input data format is supported by TensorFlow I/O (https://github.com/tensorflow/io) we recommend using that to load a Dataset instead.\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[36.93527]], dtype=float32)"
      ]
     },
     "metadata": {},
     "execution_count": 33
    }
   ],
   "source": [
    "# 模型预测\n",
    "pridict_y = model.predict(x)\n",
    "model.predict(pd.Series([20]))"
   ]
  },
  {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 模型评估\n",
    "plt.plot(x,pridict_y)\n",
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}